Celltype - consensus time group specific markers in WT ground tissue

In [1]:
rm(list=ls())
# Set the working directory to where folders named after the samples are located. 
# The folder contains spliced.mtx, unspliced.mtx, barcodes and gene id files, and json files produced by scKB that documents the sequencing stats. 
setwd("/scratch/AG_Ohler/CheWei/scKB")
In [1]:
library(tidyverse)
library(Seurat)
library(RColorBrewer)
library(future)
library(cowplot)
library(ComplexHeatmap)
library(circlize)
library(ggrepel)
#for 200gb ram 
options(future.globals.maxSize = 200000 * 1024^2)
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

✔ ggplot2 3.3.0     ✔ purrr   0.3.3
✔ tibble  3.0.1     ✔ dplyr   0.8.5
✔ tidyr   1.0.2     ✔ stringr 1.4.0
✔ readr   1.3.1     ✔ forcats 0.5.0

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()


********************************************************

Note: As of version 1.0.0, cowplot does not change the

  default ggplot2 theme anymore. To recover the previous

  behavior, execute:
  theme_set(theme_cowplot())

********************************************************


Loading required package: grid

========================================
ComplexHeatmap version 2.2.0
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
  genomic data. Bioinformatics 2016.
========================================


========================================
circlize version 0.4.8
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: http://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization 
  in R. Bioinformatics 2014.
========================================


In [2]:
rc.integrated <- readRDS("./supp_data/Ground_Tissue_Atlas.rds")
In [3]:
table(rc.integrated$celltype.anno)
Putative Quiescent Center           Stem Cell Niche                 Columella 
                     1195                      1807                         0 
         Lateral Root Cap              Atrichoblast               Trichoblast 
                        0                         0                         0 
                   Cortex                Endodermis                 Pericycle 
                    10206                     10479                         0 
                   Phloem                     Xylem                Procambium 
                        0                         0                         0 
In [4]:
# Simple QC label
rc.integrated$celltype.anno <- gsub("Putative Quiescent Center", "Quiescent Center", rc.integrated$celltype.anno, ignore.case = FALSE, perl = FALSE,
     fixed = T, useBytes = FALSE)

order <- c("Quiescent Center", "Stem Cell Niche", "Columella", "Lateral Root Cap", "Atrichoblast", "Trichoblast", "Cortex", "Endodermis", "Pericycle", "Phloem", "Xylem", "Procambium", "Unknown")
palette <- c("#BD53FF", "#DED3FE", "#5AB953", "#BFEF45", "#008080", "#21B6A8", "#82B6FF", "#0000FF","#FF9900","#E6194B", "#9A6324", "#FFE119","#EEEEEE")
rc.integrated$celltype.anno <- factor(rc.integrated$celltype.anno, levels = order[sort(match(unique(rc.integrated$celltype.anno),order))])
color <- palette[sort(match(unique(rc.integrated$celltype.anno),order))]
In [5]:
options(repr.plot.width=8, repr.plot.height=6)
DimPlot(rc.integrated, reduction = "umap", group.by = "celltype.anno", cols=color)
In [6]:
options(repr.plot.width=8, repr.plot.height=6)
time_plt <- DimPlot(rc.integrated, 
        group.by = "time.anno", 
        order = c("Maturation","Elongation","Meristem"),
        cols = c("#DCEDC8", "#42B3D5", "#1A237E"))
time_plt
In [7]:
table(rc.integrated$consensus.time.group)
  T0   T1   T2   T3   T4   T5   T6   T7   T8   T9 
2616  691 2025 3073 2884 2248 1986 2308 2706 3150 
In [8]:
options(repr.plot.width=8, repr.plot.height=6)
DimPlot(rc.integrated, reduction = "umap", group.by = "consensus.time.group", cols=brewer.pal(10,"Spectral"))
In [9]:
# FindMarkers for celltype and time combination

rc.integrated$cell_group <- paste(rc.integrated$celltype.anno, rc.integrated$consensus.time.group, sep="_")
In [10]:
table(rc.integrated$cell_group)
          Cortex_T0           Cortex_T1           Cortex_T2           Cortex_T3 
                 11                 154                 664                1391 
          Cortex_T4           Cortex_T5           Cortex_T6           Cortex_T7 
               1272                1136                1106                1401 
          Cortex_T8           Cortex_T9       Endodermis_T0       Endodermis_T1 
               1670                1401                   6                 173 
      Endodermis_T2       Endodermis_T3       Endodermis_T4       Endodermis_T5 
               1333                1673                1611                1112 
      Endodermis_T6       Endodermis_T7       Endodermis_T8       Endodermis_T9 
                880                 906                1036                1749 
Quiescent Center_T0 Quiescent Center_T1 Quiescent Center_T2 Quiescent Center_T3 
               1070                 109                   9                   6 
Quiescent Center_T4  Stem Cell Niche_T0  Stem Cell Niche_T1  Stem Cell Niche_T2 
                  1                1529                 255                  19 
 Stem Cell Niche_T3  Stem Cell Niche_T7 
                  3                   1 
In [11]:
table(rc.integrated$celltype.anno, rc.integrated$consensus.time.group)
                  
                     T0   T1   T2   T3   T4   T5   T6   T7   T8   T9
  Quiescent Center 1070  109    9    6    1    0    0    0    0    0
  Stem Cell Niche  1529  255   19    3    0    0    0    1    0    0
  Cortex             11  154  664 1391 1272 1136 1106 1401 1670 1401
  Endodermis          6  173 1333 1673 1611 1112  880  906 1036 1749
In [12]:
Idents(rc.integrated) <- "cell_group"
In [13]:
Clust_Markers <- FindAllMarkers(rc.integrated,
                                max.cells.per.ident = 5000,
                                only.pos=T, 
                               test.use="roc")
Calculating cluster Cortex_T6

Calculating cluster Endodermis_T4

Calculating cluster Quiescent Center_T0

Calculating cluster Endodermis_T5

Calculating cluster Cortex_T7

Calculating cluster Cortex_T9

Calculating cluster Endodermis_T1

Calculating cluster Cortex_T4

Calculating cluster Endodermis_T2

Calculating cluster Endodermis_T8

Calculating cluster Cortex_T5

Calculating cluster Endodermis_T7

Calculating cluster Stem Cell Niche_T1

Calculating cluster Cortex_T8

Calculating cluster Endodermis_T9

Calculating cluster Endodermis_T6

Calculating cluster Cortex_T1

Calculating cluster Stem Cell Niche_T0

Calculating cluster Cortex_T3

Calculating cluster Cortex_T2

Calculating cluster Endodermis_T3

Calculating cluster Quiescent Center_T2

Calculating cluster Quiescent Center_T1

Calculating cluster Cortex_T0

Calculating cluster Endodermis_T0

Calculating cluster Quiescent Center_T3

Calculating cluster Stem Cell Niche_T2

Calculating cluster Stem Cell Niche_T3

Calculating cluster Quiescent Center_T4

Calculating cluster Stem Cell Niche_T7

In [14]:
feature_names <- read_tsv("./supp_data/features.tsv.gz", col_names = c("gene", "Name", "Type")) %>%
  select(-Type) %>%
  distinct()
Parsed with column specification:
cols(
  gene = col_character(),
  Name = col_character(),
  Type = col_character()
)

In [15]:
Clust_Markers <- left_join(Clust_Markers, feature_names)
Joining, by = "gene"

In [16]:
Clust_Markers %>% group_by(cluster) %>% tally()
A tibble: 28 × 2
clustern
<fct><int>
Cortex_T6 197
Endodermis_T4 342
Quiescent Center_T02974
Endodermis_T5 246
Cortex_T7 206
Cortex_T9 477
Endodermis_T1 838
Cortex_T4 506
Endodermis_T2 655
Endodermis_T8 655
Cortex_T5 417
Endodermis_T7 338
Stem Cell Niche_T1 2308
Cortex_T8 392
Endodermis_T9 824
Endodermis_T6 176
Cortex_T1 893
Stem Cell Niche_T0 2342
Cortex_T3 587
Cortex_T2 602
Endodermis_T3 475
Quiescent Center_T2 693
Quiescent Center_T12494
Cortex_T0 730
Endodermis_T0 556
Quiescent Center_T3 484
Stem Cell Niche_T2 543
Stem Cell Niche_T3 321
In [17]:
Clust_Markers %>% group_by(cluster) %>% top_n(1, myAUC)
A grouped_df: 33 × 8
myAUCavg_diffpowerpct.1pct.2clustergeneName
<dbl><dbl><dbl><dbl><dbl><fct><chr><chr>
0.8561.2050160.7120.8800.260Cortex_T6 AT1G53830PME2
0.9052.0650410.8100.9620.266Endodermis_T4 AT5G13910LEP
0.9704.1276200.9400.9470.076Quiescent Center_T0AT2G28900OEP161
0.8841.8900560.7680.9790.353Endodermis_T5 AT4G16447AT4G16447
0.8541.4687300.7080.9440.335Cortex_T7 AT1G62510AT1G62510
0.9592.6683010.9180.9960.180Cortex_T9 AT5G15180PER56
0.9323.0493820.8640.9250.157Endodermis_T1 AT5G07030AT5G07030
0.8941.9113770.7880.9670.284Cortex_T4 AT5G11420AT5G11420
0.9152.3173230.8300.9840.365Endodermis_T2 AT1G11580ATPMEPCRA
0.9231.9703030.8460.9900.220Endodermis_T8 AT2G36100CASP1
0.8771.7720210.7540.9450.289Cortex_T5 AT5G11420AT5G11420
0.8771.7142000.7540.9390.222Endodermis_T7 AT5G66390PER72
0.9563.4265990.9120.9760.176Stem Cell Niche_T1 AT3G09200RPP0B
0.9242.0575100.8480.9550.173Cortex_T8 AT5G15180PER56
0.9703.2644610.9400.9830.124Endodermis_T9 AT2G39430DIR9
0.8791.3930070.7580.9950.470Endodermis_T6 AT1G02900RALF1
0.9633.4891080.9260.9810.214Cortex_T1 AT2G45050GATA2
0.9682.9619160.9360.9970.129Stem Cell Niche_T0 AT3G09200RPP0B
0.9682.8883770.9361.0000.118Stem Cell Niche_T0 AT5G16130RPS7C
0.9682.7886250.9360.9940.124Stem Cell Niche_T0 AT2G39460RPL23AA
0.9682.7265300.9360.9950.119Stem Cell Niche_T0 AT2G47610RPL7AA
0.9001.8811750.8000.8950.192Cortex_T3 AT4G15160AT4G15160
0.8822.1546040.7640.9160.319Cortex_T2 AT5G62210AT5G62210
0.9142.1057010.8280.9160.208Endodermis_T3 AT1G62480AT1G62480
0.9581.8372650.9160.3330.013Quiescent Center_T2AT2G21045HAC1
0.9483.1279190.8960.9820.173Quiescent Center_T1AT4G27090RPL14B
0.9482.5300510.8960.9910.185Quiescent Center_T1AT2G36160RPS14A
0.9482.2767800.8960.9910.203Quiescent Center_T1AT3G11940RPS5B
0.9823.9995490.9641.0000.296Cortex_T0 AT1G17285AT1G17285
0.9893.8438610.9781.0000.191Endodermis_T0 AT1G46264HSFB4
0.9711.5784380.9421.0000.101Quiescent Center_T3AT2G27660AT2G27660
0.9151.5261140.8301.0000.194Stem Cell Niche_T2 AT3G49910RPL26A
0.9801.9804350.9600.6670.015Stem Cell Niche_T3 AT1G23540PERK12
In [18]:
Clust_Markers <- separate(Clust_Markers, cluster, into=c("celltype", "stage"), sep="_", remove=F)
In [19]:
Clust_Markers <- mutate(Clust_Markers, pct.diff=pct.1-pct.2)

Clust_Markers <- arrange(Clust_Markers, desc(pct.diff)) %>%
group_by(cluster) %>%
mutate(pct.diff_rank=dplyr::row_number()) %>%
arrange(desc(avg_diff)) %>%
mutate(avg_diff_rank=dplyr::row_number()) %>%
arrange(desc(myAUC)) %>%
mutate(myAUC_rank=dplyr::row_number()) %>%
mutate(combined_rank_raw=(pct.diff_rank + avg_diff_rank + myAUC_rank)/3) %>%
arrange(combined_rank_raw) %>%
mutate(combined_rank=dplyr::row_number()) %>%
select(-combined_rank_raw) %>%
arrange(combined_rank)
Clust_Markers
A grouped_df: 22271 × 15
myAUCavg_diffpowerpct.1pct.2clustercelltypestagegeneNamepct.diffpct.diff_rankavg_diff_rankmyAUC_rankcombined_rank
<dbl><dbl><dbl><dbl><dbl><fct><chr><chr><chr><chr><dbl><int><int><int><int>
0.9052.0650410.8100.9620.266Endodermis_T4 Endodermis T4AT5G13910LEP 0.696 2 2 11
0.9633.4891080.9260.9810.214Cortex_T1 Cortex T1AT2G45050GATA2 0.767 3 5 11
0.9142.1057010.8280.9160.208Endodermis_T3 Endodermis T3AT1G62480AT1G624800.708 1 8 11
0.9323.0493820.8640.9250.157Endodermis_T1 Endodermis T1AT5G07030AT5G070300.768 2 10 11
0.8772.0066300.7540.9390.270Cortex_T4 Cortex T4AT1G41830SKS6 0.669 2 11 31
0.8975.5460180.7940.9470.208Stem Cell Niche_T2 Stem Cell Niche T2AT1G56070LOS1 0.73910 3 51
0.9714.6290080.9421.0000.123Endodermis_T0 Endodermis T0AT4G29050AT4G290500.877 1 6131
0.8882.4255910.7760.8870.164Endodermis_T2 Endodermis T2AT3G23730XTH16 0.723 1 15 41
0.8551.9278370.7100.9100.270Endodermis_T5 Endodermis T5AT2G23560MES7 0.640 2 12 61
0.8531.4809190.7060.9420.339Cortex_T6 Cortex T6AT2G39010PIP2-6 0.603 2 16 21
0.8701.8409860.7400.9130.276Cortex_T5 Cortex T5AT1G41830SKS6 0.637 2 18 31
0.8281.9280550.6560.7620.204Cortex_T7 Cortex T7AT3G18200AT3G182000.558 9 5 91
0.8252.0488600.6500.7700.201Endodermis_T6 Endodermis T6AT4G15960AT4G159600.569 7 9 81
0.9313.0832620.8620.9350.172Cortex_T9 Cortex T9AT5G44480DUR 0.763 4 15 61
0.9704.3185000.9401.0000.128Cortex_T0 Cortex T0AT4G21960PER42 0.872 1 17111
0.9704.1276200.9400.9470.076Quiescent Center_T0Quiescent CenterT0AT2G28900OEP161 0.87120 9 11
0.8702.1440050.7400.9140.299Cortex_T3 Cortex T3AT5G62210AT5G622100.615 9 12111
0.9585.9591160.9161.0000.233Stem Cell Niche_T3 Stem Cell Niche T3AT4G31890AT4G318900.76715 11 71
0.8662.4431800.7320.8480.254Cortex_T2 Cortex T2AT5G14020AT5G140200.594 7 28 31
0.9694.5105910.9380.9720.270Endodermis_T9 Endodermis T9AT1G02500SAM1 0.70246 1 21
0.9545.5965750.9080.8330.058Quiescent Center_T3Quiescent CenterT3AT4G25170AT4G251700.77515 23121
0.8952.1564380.7900.8110.157Cortex_T8 Cortex T8AT1G14870PCR2 0.654 7 38 61
0.8711.7409780.7420.9370.227Endodermis_T7 Endodermis T7AT2G36100CASP1 0.710 2 50 31
0.8422.5773150.6840.6820.140Endodermis_T8 Endodermis T8AT1G03850AT1G038500.54235 16201
0.9553.5174420.9100.9880.162Stem Cell Niche_T1 Stem Cell Niche T1AT2G19730RPL28A 0.826 9 82 21
0.9044.9951430.8080.8890.232Quiescent Center_T2Quiescent CenterT2AT1G30230AT1G302300.65721 55211
0.9443.3122690.8880.9720.174Quiescent Center_T1Quiescent CenterT1AT5G15200RPS9B 0.79815101 51
0.9663.0744980.9320.9970.119Stem Cell Niche_T0 Stem Cell Niche T0AT4G18100RPL32A 0.87836 90121
0.8902.1501640.7800.8770.222Endodermis_T3 Endodermis T3AT3G52920AT3G529200.655 8 3 52
0.9583.2670700.9160.9940.244Cortex_T1 Cortex T1AT3G60530GATA4 0.750 6 13 22
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
0.7090.43988410.4180.6360.287Quiescent Center_T0Quiescent CenterT0AT5G13970AT5G13970 0.3492843289028082945
0.2930.85832740.4140.1160.383Quiescent Center_T0Quiescent CenterT0AT3G05770AT3G05770-0.2672967262729522946
0.7160.34642740.4320.4800.222Quiescent Center_T0Quiescent CenterT0AT2G31390AT2G31390 0.2582924293026942947
0.7120.36794750.4240.6230.296Quiescent Center_T0Quiescent CenterT0AT1G12520CCS 0.3272876292227582948
0.7090.29236280.4180.6260.265Quiescent Center_T0Quiescent CenterT0AT4G38570PIS2 0.3612815295528102949
0.7100.50918970.4200.1490.062Quiescent Center_T0Quiescent CenterT0AT1G23750AT1G23750 0.0872944285527892950
0.7020.44291520.4040.6410.269Quiescent Center_T0Quiescent CenterT0AT1G52370AT1G52370 0.3722780288929282951
0.7040.29674550.4080.6810.303Quiescent Center_T0Quiescent CenterT0AT2G39440AT2G39440 0.3782762295028912952
0.7030.63569330.4060.4640.189Quiescent Center_T0Quiescent CenterT0AT5G50180AT5G50180 0.2752917278229072953
0.7030.25118030.4060.5220.137Quiescent Center_T0Quiescent CenterT0AT4G28400AT4G28400 0.3852743297329092954
0.7070.47447630.4140.5840.295Quiescent Center_T0Quiescent CenterT0AT1G65220AT1G65220 0.2892910287028462955
0.2780.74115960.4440.0810.253Quiescent Center_T0Quiescent CenterT0AT5G03180AT5G03180-0.1722951271629602956
0.2930.69143920.4140.0790.216Quiescent Center_T0Quiescent CenterT0AT2G25565AT2G25565-0.1372948274829532957
0.7060.27356270.4120.6640.321Quiescent Center_T0Quiescent CenterT0AT1G09150AT1G09150 0.3432850296428722958
0.7030.36239660.4060.5590.218Quiescent Center_T0Quiescent CenterT0AT1G16916AT1G16916 0.3412854292729082959
0.7070.36636190.4140.5360.266Quiescent Center_T0Quiescent CenterT0AT5G45750RABA1C 0.2702921292528472960
0.2800.61869780.4400.0700.305Quiescent Center_T0Quiescent CenterT0AT2G31590AT2G31590-0.2352961279129592961
0.2640.62419450.4720.0090.199Quiescent Center_T0Quiescent CenterT0AT4G31441AT4G31441-0.1902954279029682962
0.2940.55979000.4120.1250.278Quiescent Center_T0Quiescent CenterT0AT3G15340PPI2 -0.1532949282629502963
0.2100.54833610.5800.1000.524Quiescent Center_T0Quiescent CenterT0AT2G47890COL13 -0.4242973283229732964
0.2440.52813440.5120.0210.300Quiescent Center_T0Quiescent CenterT0AT3G10450SCPL7 -0.2792969283929712965
0.2780.50255060.4440.0470.321Quiescent Center_T0Quiescent CenterT0AT1G63390AT1G63390-0.2742968285929612966
0.2760.47691850.4480.1270.356Quiescent Center_T0Quiescent CenterT0AT1G01670PUB56 -0.2292960286729622967
0.2720.47285220.4560.1710.428Quiescent Center_T0Quiescent CenterT0AT3G48115AT3G48115-0.2572964287429652968
0.2900.44951960.4200.1960.481Quiescent Center_T0Quiescent CenterT0AT2G45160SCL27 -0.2852970288129572969
0.2940.38653480.4120.0190.191Quiescent Center_T0Quiescent CenterT0AT3G07730AT3G07730-0.1722953291329512970
0.2190.44723110.5620.1000.464Quiescent Center_T0Quiescent CenterT0AT5G37740AT5G37740-0.3642972288529722971
0.2950.37097540.4100.0720.310Quiescent Center_T0Quiescent CenterT0AT5G02645AT5G02645-0.2382962292129492972
0.2920.32141300.4160.0720.206Quiescent Center_T0Quiescent CenterT0AT5G02440AT5G02440-0.1342947294029542973
0.2920.25048670.4160.1440.455Quiescent Center_T0Quiescent CenterT0AT2G28320AT2G28320-0.3112971297429552974
In [20]:
table(Clust_Markers$stage)
  T0   T1   T2   T3   T4   T5   T6   T7   T8   T9 
6602 6533 2493 1867  848  663  373  544 1047 1301 
In [21]:
Clust_Markers$stage <- factor(Clust_Markers$stage, levels=c("T0", "T1", "T2", "T3", "T4", "T5", "T6", "T7", "T8", "T9"))
In [22]:
rc.integrated$consensus.time.group <- factor(rc.integrated$consensus.time.group, levels=c("T0", "T1", "T2", "T3", "T4", "T5", "T6", "T7", "T8", "T9"))
In [23]:
Idents(rc.integrated) <- "consensus.time.group"
In [24]:
# pseudobulk of each stage

  afm <- as.matrix(rc.integrated@assays$integrated@data)
  pooled <- matrix(nrow=nrow(afm), ncol = 0)

  for (i in unique(rc.integrated@meta.data$consensus.time.group)) {
    m <- afm[,which(rc.integrated@meta.data$consensus.time.group==i)]
    pooled <- cbind(pooled, rowSums(m)/ncol(m))
  }
In [25]:
unique(rc.integrated@meta.data$consensus.time.group)
  1. T6
  2. T4
  3. T0
  4. T5
  5. T7
  6. T9
  7. T1
  8. T2
  9. T8
  10. T3
Levels:
  1. 'T0'
  2. 'T1'
  3. 'T2'
  4. 'T3'
  5. 'T4'
  6. 'T5'
  7. 'T6'
  8. 'T7'
  9. 'T8'
  10. 'T9'
In [26]:
colnames(pooled) <- unique(rc.integrated@meta.data$consensus.time.group)
In [27]:
pooled_df <- as_tibble(pooled, rownames = "gene")
In [28]:
pooled_df_long <- pivot_longer(pooled_df, cols = 2:ncol(pooled_df), names_to = "stage", values_to = "expression")
In [29]:
long_order <- tibble(stage=paste("T", rep(0:9), sep=""), 
                     order=rep(1:10))
In [30]:
long_order
A tibble: 10 × 2
stageorder
<chr><int>
T0 1
T1 2
T2 3
T3 4
T4 5
T5 6
T6 7
T7 8
T8 9
T910
In [31]:
(pooled_df_long <- left_join(pooled_df_long, long_order) %>%
arrange(order))
Joining, by = "stage"

A tibble: 175130 × 4
genestageexpressionorder
<chr><chr><dbl><int>
AT1G05260T0-1.35167261
AT3G59370T0-2.15137581
AT2G36100T0-0.63837631
AT1G12080T0-2.49769001
AT1G12090T0-1.61717991
AT4G11290T0-1.33161021
AT5G42180T0-0.62866451
AT5G66390T0-0.78788781
AT2G32300T0-1.20410231
AT2G02130T0-2.31999301
AT3G11550T0-0.49642361
AT4G35350T0-0.57896691
AT1G30750T0-0.37385431
AT4G40090T0-0.95990671
AT1G20850T0-0.59483541
AT2G39430T0-0.42484591
AT1G75750T0-4.22722391
AT4G13580T0-0.50168611
AT4G25820T0-0.77242421
AT4G23690T0-1.45741341
AT1G26820T0-0.65748671
AT5G64100T0-2.26739611
AT1G65310T0-1.04704871
AT2G40113T0-0.39173061
AT5G17820T0-2.45716231
AT3G24020T0-0.45732141
AT5G04200T0-0.62870781
AT3G54580T0-1.19906051
AT3G09925T0-1.07566981
AT5G46900T0-2.83118161
⋮⋮⋮⋮
AT4G22220T9-0.0907664110
AT2G23090T9 0.0741178010
AT2G46490T9-0.4030605010
AT3G27240T9 0.4580462710
AT1G07140T9 0.0232015510
AT1G76200T9 0.4290114010
AT5G08670T9 0.3172844810
AT1G04357T9-0.0274950110
AT1G64230T9 0.1356091110
AT2G02050T9 0.3046970310
AT3G09840T9 0.3822333010
AT5G47030T9 0.1372630610
AT1G10590T9-0.3274218610
AT1G08480T9 0.2959985110
AT5G61330T9 0.0297926010
AT4G02580T9-0.0272667110
AT5G08060T9 0.4358835210
AT1G75950T9-0.2218126710
AT2G20360T9 0.3588552610
AT4G30010T9 0.3350593410
AT4G06395T9-0.0434014610
AT3G55440T9 0.1271603110
AT3G03100T9 0.4447506410
AT5G54760T9-0.0922013310
AT2G33040T9 0.3402451210
AT2G42680T9 0.0553335510
AT5G11770T9 0.4194712310
AT5G08290T9-0.0303622610
AT5G53300T9 0.0750379410
AT5G64400T9 0.2573953410
In [32]:
(long_corrs <- pooled_df_long %>%
group_by(gene) %>%
summarise(long_cor = cor(order, expression, method = "spearman")))
A tibble: 17513 × 2
genelong_cor
<chr><dbl>
AT1G01010 0.63636364
AT1G01020-0.40606061
AT1G01030-0.44242424
AT1G01040 0.68484848
AT1G01050-0.91515152
AT1G01070 0.17575758
AT1G01080 0.28484848
AT1G01090-0.89090909
AT1G01100-0.91515152
AT1G01110-0.70909091
AT1G01120 0.87878788
AT1G01130 0.20000000
AT1G01160-0.44242424
AT1G01170 0.18787879
AT1G01180 0.91515152
AT1G01200 0.55151515
AT1G01210-0.15151515
AT1G01220-0.87878788
AT1G01225-0.57575758
AT1G01230 0.05454545
AT1G01240 0.63636364
AT1G01260-0.98787879
AT1G01290-0.60000000
AT1G01300-0.93939394
AT1G01310 0.84242424
AT1G01320 0.10303030
AT1G01340 0.96363636
AT1G01350 0.57575758
AT1G01360-0.61212121
AT1G01370-0.58787879
⋮⋮
AT5G67320 0.17575758
AT5G67330 0.92727273
AT5G67340 0.92727273
AT5G67360-0.52727273
AT5G67370 0.89090909
AT5G67380 0.56363636
AT5G67385-0.23636364
AT5G67390-0.44242424
AT5G67400 0.80606061
AT5G67410-0.75757576
AT5G67420-0.18787879
AT5G67440 0.11515152
AT5G67450 0.18787879
AT5G67460-0.13939394
AT5G67470 0.01818182
AT5G67488 0.04242424
AT5G67500 0.89090909
AT5G67510-0.13939394
AT5G67520 0.84242424
AT5G67530-0.87878788
AT5G67540-0.63636364
AT5G67560 0.93939394
AT5G67570-0.95151515
AT5G67580 0.57575758
AT5G67590 1.00000000
AT5G67600 0.52727273
AT5G67610-0.07878788
AT5G67620 0.57575758
AT5G67630-0.12727273
AT5G67640-0.07878788
In [33]:
Clust_Markers <- left_join(Clust_Markers, long_corrs) %>%
mutate(abs_long_cor=abs(long_cor)) %>%
arrange(-abs_long_cor)
Clust_Markers
Joining, by = "gene"

A grouped_df: 22271 × 17
myAUCavg_diffpowerpct.1pct.2clustercelltypestagegeneNamepct.diffpct.diff_rankavg_diff_rankmyAUC_rankcombined_ranklong_corabs_long_cor
<dbl><dbl><dbl><dbl><dbl><fct><chr><fct><chr><chr><dbl><int><int><int><int><dbl><dbl>
0.8791.39300700.7580.9950.470Endodermis_T6 Endodermis T6AT1G02900RALF1 0.525 19 65 1 9 11
0.8401.41899370.6800.9560.500Endodermis_T7 Endodermis T7AT5G58375AT5G583750.456 41101 520 11
0.8281.12501080.6560.9910.469Endodermis_T7 Endodermis T7AT1G02900RALF1 0.522 15157 728 11
0.8773.08794770.7540.9240.398Cortex_T9 Cortex T9AT5G60360AALP 0.526100 13 4029 11
0.8331.15649660.6660.9880.465Endodermis_T5 Endodermis T5AT1G02900RALF1 0.523 37110 1631 11
0.8031.08833000.6060.8170.317Endodermis_T7 Endodermis T7AT5G63905AT5G639050.500 22166 1638 11
0.7951.99642750.5900.8010.286Cortex_T8 Cortex T8AT2G15370FUT5 0.515 60 72 8141 11
0.9113.36345130.8220.9170.314Endodermis_T9 Endodermis T9AT3G20510FAX6 0.603113 62 5342 11
0.8041.79671790.6080.8270.314Endodermis_T8 Endodermis T8AT5G63905AT5G639050.513 58123 8442 11
0.9434.00046410.8860.9570.216Stem Cell Niche_T1Stem Cell NicheT1AT2G31610RPS3A 0.741191 10 7043-11
0.8581.77017280.7160.9490.433Cortex_T8 Cortex T8AT3G26520TIP1-2 0.516 59147 2145 11
0.8532.56055630.7060.8840.306Cortex_T9 Cortex T9AT1G11310MLO2 0.578 65 86 6349 11
0.9312.38875770.8620.9870.437Cortex_T9 Cortex T9AT3G26520TIP1-2 0.550 80126 950 11
0.9431.81660620.8861.0000.219Cortex_T0 Cortex T0AT1G54000GLL22 0.781 31265 4550-11
0.9394.05437010.8780.9760.483Endodermis_T9 Endodermis T9AT5G55850NOI 0.493249 8 2854 11
0.8711.52866240.7420.9630.497Endodermis_T8 Endodermis T8AT5G58375AT5G583750.466124171 954 11
0.8463.09233240.6920.9300.454Cortex_T9 Cortex T9AT3G30390AVT6A 0.476148 12 7156 11
0.8643.31601460.7280.8950.327Endodermis_T9 Endodermis T9AT2G24940MP3 0.568145 6611460 11
0.8592.39389250.7180.8790.288Cortex_T9 Cortex T9AT2G15370FUT5 0.591 56124 5661 11
0.7952.09605100.5900.6890.262Cortex_T8 Cortex T8AT2G40110AT2G401100.427133 49 8061 11
0.8493.95077650.6980.8480.315Endodermis_T9 Endodermis T9AT3G46000ADF2 0.533193 1613762 11
0.8130.60421550.6260.8710.349Endodermis_T7 Endodermis T7AT2G24940MP3 0.522 16264 1264 11
0.8993.72283080.7980.9370.447Endodermis_T9 Endodermis T9AT3G61260AT3G612600.490253 34 6465 11
0.7501.66499550.5000.8370.499Cortex_T7 Cortex T7AT1G05850CTL1 0.338136 23 8271 11
0.9612.74510140.9220.9900.130Stem Cell Niche_T0Stem Cell NicheT0AT2G34480AT2G344800.860100229 8371-11
0.8532.64668080.7060.9370.456Cortex_T9 Cortex T9AT4G39080VHA-A3 0.481139 68 6272 11
0.7892.04881520.5780.8820.464Cortex_T8 Cortex T8AT5G34850PAP26 0.418148 56 8975 11
0.9453.11806940.8900.9730.177Stem Cell Niche_T1Stem Cell NicheT1AT2G34480AT2G344800.796 69253 5875-11
0.8642.51248160.7280.9630.482Cortex_T9 Cortex T9AT5G43600UAH 0.481140 95 4877 11
0.7921.67489990.5840.7930.339Endodermis_T8 Endodermis T8AT3G20510FAX6 0.45414114210981 11
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
0.7101.64757510.4200.6250.170Stem Cell Niche_T0 Stem Cell Niche T0AT3G08000AT3G08000 0.4551753141521501924-0.0060606060.006060606
0.7121.07720930.4240.5370.078Stem Cell Niche_T1 Stem Cell Niche T1AT5G48100TT10 0.4591566171621181947 0.0060606060.006060606
0.7660.89092120.5320.5370.178Stem Cell Niche_T1 Stem Cell Niche T1AT5G14960E2FD 0.3592065186115271962-0.0060606060.006060606
0.7142.38769530.4280.5410.206Quiescent Center_T1Quiescent CenterT1AT5G02710AT5G02710 0.3352340 86422741996-0.0060606060.006060606
0.7791.02604620.5580.7510.202Quiescent Center_T0Quiescent CenterT0AT3G50190AT3G50190 0.5491575248216632012 0.0060606060.006060606
0.7420.92161260.4840.6330.156Quiescent Center_T1Quiescent CenterT1AT1G71420PCMP-H70 0.4771559205719542039-0.0060606060.006060606
0.7490.73867910.4980.6900.303Stem Cell Niche_T1 Stem Cell Niche T1AT3G49680BCAT3 0.3871953197517022049-0.0060606060.006060606
0.7351.76602910.4700.5780.205Quiescent Center_T1Quiescent CenterT1AT3G45050AT3G45050 0.3732217145620432085 0.0060606060.006060606
0.7061.60261200.4120.6700.282Stem Cell Niche_T0 Stem Cell Niche T0AT3G49680BCAT3 0.3882113146122252085-0.0060606060.006060606
0.7340.57670490.4680.6420.180Quiescent Center_T1Quiescent CenterT1AT3G03950ECT1 0.4621662227220602179 0.0060606060.006060606
0.7540.38596440.5080.2670.015Stem Cell Niche_T1 Stem Cell Niche T1AT5G44580AT5G44580 0.2522238222516562194-0.0060606060.006060606
0.7061.07565430.4120.5000.088Stem Cell Niche_T0 Stem Cell Niche T0AT3G44290NAC60 0.4122001195822322215-0.0060606060.006060606
0.7330.42125280.4660.4040.066Stem Cell Niche_T1 Stem Cell Niche T1AT5G62710AT5G62710 0.3382131219918762217-0.0060606060.006060606
0.7411.48266010.4820.6390.142Quiescent Center_T0Quiescent CenterT0AT4G21820AT4G21820 0.4971991201822482233-0.0060606060.006060606
0.8050.41909040.6100.7070.216Quiescent Center_T0Quiescent CenterT0AT5G11650AT5G11650 0.4912049289913222236-0.0060606060.006060606
0.7141.14193590.4280.5090.220Stem Cell Niche_T0 Stem Cell Niche T0AT5G11650AT5G11650 0.2892303190320952248-0.0060606060.006060606
0.2290.91177470.5420.1020.491Stem Cell Niche_T1 Stem Cell Niche T1AT1G23380KNAT6 -0.3892302184622992263 0.0060606060.006060606
0.7650.59646990.5300.4400.104Quiescent Center_T1Quiescent CenterT1AT5G25170AT5G25170 0.3362337225816832272-0.0060606060.006060606
0.2440.58184560.5120.0900.470Stem Cell Niche_T1 Stem Cell Niche T1AT4G29810ATMKK2 -0.3802300209422952298 0.0060606060.006060606
0.2780.49575630.4440.1530.373Stem Cell Niche_T1 Stem Cell Niche T1AT5G35525PCR3 -0.2202266215222772299-0.0060606060.006060606
0.7121.27176910.4240.4590.122Quiescent Center_T1Quiescent CenterT1AT4G37380ELI1 0.3372335184023062328 0.0060606060.006060606
0.2931.28120980.4140.1650.420Quiescent Center_T1Quiescent CenterT1AT2G27830AT2G27830-0.2552461182424452392-0.0060606060.006060606
0.2420.34763670.5160.0920.365Quiescent Center_T1Quiescent CenterT1AT4G10140AT4G10140-0.2732466243124832490 0.0060606060.006060606
0.2480.27327630.5040.1380.489Quiescent Center_T1Quiescent CenterT1AT1G23380KNAT6 -0.3512485247724802494 0.0060606060.006060606
0.7460.44523420.4920.6930.177Quiescent Center_T0Quiescent CenterT0AT1G30520AAE14 0.5161850288621812510 0.0060606060.006060606
0.7420.93930470.4840.6660.185Quiescent Center_T0Quiescent CenterT0AT3G45050AT3G45050 0.4812124256622412519 0.0060606060.006060606
0.7071.48655090.4140.6360.166Quiescent Center_T0Quiescent CenterT0AT3G15420AT3G15420 0.4702216201328382576 0.0060606060.006060606
0.7271.19022690.4540.5150.109Quiescent Center_T0Quiescent CenterT0AT3G09910RABC2B 0.4062631234224842692 0.0060606060.006060606
0.7330.85779730.4660.7070.271Quiescent Center_T0Quiescent CenterT0AT2G07776AT2G07776 0.4362468262923782698 0.0060606060.006060606
0.7131.03769570.4260.6450.232Quiescent Center_T0Quiescent CenterT0AT2G16880AT2G16880 0.4132595246927342794 0.0060606060.006060606
In [34]:
genes_to_plt_df <- filter(Clust_Markers, celltype %in% c("Endodermis")) %>%
group_by(stage) %>%
top_n(20, -combined_rank) %>%
top_n(10, abs_long_cor) %>%
ungroup() %>%
arrange(stage)
# genes manually added to plotting list - these are known markers we want to make sure are there
to_add <- filter(Clust_Markers, celltype %in% c("Endodermis")) %>% filter(Name=="SCR" | Name=="MYB36" | Name=="CASP4")

(genes_to_plt_df <- bind_rows(genes_to_plt_df, to_add) %>% 
 ungroup() %>% 
 group_by(gene) %>%
 top_n(1, -combined_rank) %>% # order genes by stage of highest rank
 ungroup() %>% arrange(stage))

genes_to_plt <- unique(genes_to_plt_df$gene)

genes_to_plt_endo <- rev(genes_to_plt) # order to pointing upwards
A tibble: 91 × 17
myAUCavg_diffpowerpct.1pct.2clustercelltypestagegeneNamepct.diffpct.diff_rankavg_diff_rankmyAUC_rankcombined_ranklong_corabs_long_cor
<dbl><dbl><dbl><dbl><dbl><fct><chr><fct><chr><chr><dbl><int><int><int><int><dbl><dbl>
0.9793.5409770.9581.0000.251Endodermis_T0EndodermisT0AT3G56220AT3G562200.749 6637 713-0.91515150.9151515
0.9875.2821130.9741.0000.333Endodermis_T0EndodermisT0AT3G52960PRXIIE 0.667162 2 220-0.90303030.9030303
0.9694.5821150.9381.0000.245Endodermis_T0EndodermisT0AT5G48660AT5G486600.755 61 716 9-0.89090910.8909091
0.9763.6588230.9521.0000.217Endodermis_T0EndodermisT0AT2G20515AT2G205150.783 4729 8 8-0.87878790.8787879
0.9672.8541380.9341.0000.219Endodermis_T0EndodermisT0AT2G45050GATA2 0.781 49652416-0.87878790.8787879
0.9543.3305300.9081.0000.248Endodermis_T0EndodermisT0AT3G60530GATA4 0.752 64434517-0.85454550.8545455
0.9432.4019870.8861.0000.128Endodermis_T0EndodermisT0AT4G21960PER42 0.872 2887419-0.84242420.8424242
0.9893.8438610.9781.0000.191Endodermis_T0EndodermisT0AT1G46264HSFB4 0.809 3025 1 3-0.79393940.7939394
0.9693.6482400.9381.0000.210Endodermis_T0EndodermisT0AT3G05190AT3G051900.790 42301712-0.79393940.7939394
0.8802.8909930.7600.7860.178Endodermis_T1EndodermisT1AT5G27670HTA7 0.608 49233217-0.95151520.9515152
0.9142.8895640.8280.9020.263Endodermis_T1EndodermisT1AT1G14900HMGA 0.639 3424 8 6-0.93939390.9393939
0.9182.7419480.8360.9190.258Endodermis_T1EndodermisT1AT5G59910HTB4 0.661 2243 6 8-0.92727270.9272727
0.8883.3042990.7760.9020.297Endodermis_T1EndodermisT1AT2G22230AT2G222300.605 54 323 9-0.91515150.9151515
0.8832.8008170.7660.9360.323Endodermis_T1EndodermisT1AT2G43360BIO2 0.613 46352818-0.89090910.8909091
0.9323.0493820.8640.9250.157Endodermis_T1EndodermisT1AT5G07030AT5G070300.768 210 1 1-0.85454550.8545455
0.9023.0552660.8040.9190.272Endodermis_T1EndodermisT1AT5G28640GIF1 0.647 28 915 2-0.85454550.8545455
0.9122.8476390.8240.9360.287Endodermis_T1EndodermisT1AT5G23860TUBB8 0.649 272910 7-0.85454550.8545455
0.9132.6249920.8260.9130.276Endodermis_T1EndodermisT1AT3G45980H2B 0.637 3558 916-0.81818180.8181818
0.9292.6043590.8580.9310.161Endodermis_T1EndodermisT1AT3G54560H2AV 0.770 162 2 5-0.80606060.8060606
0.8641.8588600.7280.9520.366Endodermis_T2EndodermisT2AT4G16265NRPB9B 0.586 34801614-0.91515150.9151515
0.8681.9219040.7360.8900.282Endodermis_T2EndodermisT2AT5G55730FLA1 0.608 25731211-0.90303030.9030303
0.8732.6391950.7460.9430.458Endodermis_T2EndodermisT2AT1G54580ACP2 0.485117 8 917-0.90303030.9030303
0.8282.4094040.6560.8750.346Endodermis_T2EndodermisT2AT4G39900AT4G399000.529 68165819-0.86666670.8666667
0.8222.8420890.6440.8090.196Endodermis_T2EndodermisT2AT4G17970ALMT12 0.613 23 47010-0.79393940.7939394
0.8262.7820400.6520.7970.134Endodermis_T2EndodermisT2AT1G65900AT1G659000.663 6 560 5-0.75757580.7575758
0.8882.4255910.7760.8870.164Endodermis_T2EndodermisT2AT3G23730XTH16 0.723 115 4 1-0.74545450.7454545
0.9092.1926740.8180.9500.255Endodermis_T2EndodermisT2AT5G63660PDF2.5 0.695 241 2 4-0.73333330.7333333
0.8412.3098410.6820.9320.413Endodermis_T2EndodermisT2AT1G13280AOC4 0.519 72263618-0.73333330.7333333
0.8302.3566820.6600.8840.388Endodermis_T2EndodermisT2AT3G54220SCR 0.496104205127-0.51515150.5151515
0.8301.9453940.6600.8480.206Endodermis_T3EndodermisT3AT5G45200AT5G452000.642 12305216-0.69696970.6969697
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
0.8171.6146350.6340.9170.467Endodermis_T6EndodermisT6AT5G25770AT5G257700.45047 3713150.70909090.7090909
0.8401.4189940.6800.9560.500Endodermis_T7EndodermisT7AT5G58375AT5G583750.45641101 5201.00000001.0000000
0.8631.7284070.7260.9700.498Endodermis_T7EndodermisT7AT3G06125AT3G061250.47232 52 4 90.98787880.9878788
0.7832.0314730.5660.8540.394Endodermis_T7EndodermisT7AT5G21090LRR1 0.46039 2528110.98787880.9878788
0.7711.8340360.5420.8430.379Endodermis_T7EndodermisT7AT5G42030ABIL4 0.46438 4442160.98787880.9878788
0.8771.7142000.7540.9390.222Endodermis_T7EndodermisT7AT5G66390PER72 0.717 1 55 1 20.97575760.9757576
0.8031.6911730.6060.9020.446Endodermis_T7EndodermisT7AT3G09270GSTU8 0.45640 5915140.95151520.9515152
0.8161.7262620.6320.8520.296Endodermis_T7EndodermisT7AT4G23493AT4G234930.556 9 5310 60.93939390.9393939
0.7901.8530270.5800.6660.234Endodermis_T7EndodermisT7AT5G08240AT5G082400.43251 4122150.87878790.8787879
0.8371.7559290.6740.6990.131Endodermis_T7EndodermisT7AT4G21620AT4G216200.568 7 49 6 30.86666670.8666667
0.7961.8947450.5920.7990.318Endodermis_T7EndodermisT7AT2G15340AT2G153400.48128 3519 70.86666670.8666667
0.8191.7151910.6380.8000.310Endodermis_T7EndodermisT7AT4G34230CAD5 0.49024 54 8 80.86666670.8666667
0.8472.1807720.6940.8420.189Endodermis_T8EndodermisT8AT3G03520NPC3 0.65315 5614 40.97575760.9757576
0.8212.5212370.6420.7090.163Endodermis_T8EndodermisT8AT3G14280AT3G142800.54634 1950 90.95151520.9515152
0.8412.0476160.6820.8600.183Endodermis_T8EndodermisT8AT4G13580DIR18 0.677 9 7821100.90303030.9030303
0.8422.5773150.6840.6820.140Endodermis_T8EndodermisT8AT1G03850AT1G038500.54235 1620 10.89090910.8909091
0.8212.2663730.6420.7280.206Endodermis_T8EndodermisT8AT4G37790HAT22 0.52253 4151160.85454550.8545455
0.9231.9703030.8460.9900.220Endodermis_T8EndodermisT8AT2G36100CASP1 0.770 2 90 1 70.84242420.8424242
0.8291.9981270.6580.8690.146Endodermis_T8EndodermisT8AT3G24020DIR16 0.723 6 8334120.84242420.8424242
0.9623.3730940.9240.9770.282Endodermis_T9EndodermisT9AT4G34050CCoAOMT1 0.69552 61 4 90.98787880.9878788
0.9134.0303380.8260.9160.262Endodermis_T9EndodermisT9AT5G44790RAN1 0.65473 1149140.98787880.9878788
0.9163.6060870.8320.9490.266Endodermis_T9EndodermisT9AT1G80640AT1G806400.68359 4346200.98787880.9878788
0.9643.2925320.9280.9780.192Endodermis_T9EndodermisT9AT2G27370CASP3 0.78620 68 3 70.97575760.9757576
0.9462.9517740.8920.9630.134Endodermis_T9EndodermisT9AT2G28671AT2G286710.829 9 9822120.96363640.9636364
0.9553.3963810.9100.9620.130Endodermis_T9EndodermisT9AT1G44970PER9 0.832 7 60 9 40.95151520.9515152
0.9443.4096770.8880.9390.204Endodermis_T9EndodermisT9AT5G65020ANN2 0.73534 5824 80.95151520.9515152
0.9703.2644610.9400.9830.124Endodermis_T9EndodermisT9AT2G39430DIR9 0.859 1 70 1 30.79393940.7939394
0.9482.9459620.8960.9650.115Endodermis_T9EndodermisT9AT3G11550CASP2 0.850 510019100.79393940.7939394
0.8953.4275540.7900.8950.108Endodermis_T9EndodermisT9AT4G02090AT4G020900.78719 5770170.79393940.7939394
0.9523.2417740.9040.9640.106Endodermis_T9EndodermisT9AT5G06200CASP4 0.858 2 7315 60.76969700.7696970
In [35]:
genes_to_plt_df <- filter(Clust_Markers, celltype %in% c("Cortex")) %>%
group_by(stage) %>%
top_n(20, -combined_rank) %>%
top_n(10, abs_long_cor) %>%
ungroup() %>%
arrange(stage)

# to add AT4G09760 AT5G02000

to_add <- filter(Clust_Markers, celltype %in% c("Cortex")) %>% filter(gene %in% c("AT4G09760", "AT5G02000", "AT5G64620"))

(genes_to_plt_df <- bind_rows(genes_to_plt_df, to_add) %>% 
 ungroup() %>% 
 group_by(gene) %>%
 top_n(1, -combined_rank) %>%
 ungroup() %>% arrange(stage))



(genes_to_plt_df <- genes_to_plt_df %>% 
 ungroup() %>% 
 group_by(gene) %>%
 top_n(1, -combined_rank) %>%
 ungroup() %>% arrange(stage))

genes_to_plt <- unique(genes_to_plt_df$gene)

genes_to_plt_cortex <- genes_to_plt
A tibble: 92 × 17
myAUCavg_diffpowerpct.1pct.2clustercelltypestagegeneNamepct.diffpct.diff_rankavg_diff_rankmyAUC_rankcombined_ranklong_corabs_long_cor
<dbl><dbl><dbl><dbl><dbl><fct><chr><fct><chr><chr><dbl><int><int><int><int><dbl><dbl>
0.9753.9866480.9501.0000.268Cortex_T0CortexT0AT3G18000NMT1 0.732 55 36 5 7-0.97575760.9757576
0.9432.7802270.8861.0000.237Cortex_T0CortexT0AT1G68560XYL1 0.763 37132 4320-0.97575760.9757576
0.9744.5380780.9481.0000.265Cortex_T0CortexT0AT2G24280AT2G242800.735 52 7 7 3-0.92727270.9272727
0.9533.2191540.9061.0000.248Cortex_T0CortexT0AT3G09455OST4C 0.752 43 95 2612-0.91515150.9151515
0.9484.5378720.8961.0000.333Cortex_T0CortexT0AT3G52960PRXIIE 0.667125 8 3011-0.90303030.9030303
0.9194.3234960.8381.0000.304Cortex_T0CortexT0AT3G54960PDIL1-3 0.696 87 1610219-0.90303030.9030303
0.9314.1301250.8620.9090.215Cortex_T0CortexT0AT2G34020AT2G340200.694 88 28 6717-0.89090910.8909091
0.9803.9391010.9601.0000.217Cortex_T0CortexT0AT2G20515AT2G205150.783 27 38 2 5-0.87878790.8787879
0.9793.6179220.9581.0000.219Cortex_T0CortexT0AT2G45050GATA2 0.781 30 61 3 6-0.87878790.8787879
0.9333.7594400.8661.0000.283Cortex_T0CortexT0AT5G05890UGT76C5 0.717 63 50 6015-0.87878790.8787879
0.9233.0227780.8460.9550.306Cortex_T1CortexT1AT4G34200PGDH1 0.649 44 33 1614-0.97575760.9757576
0.8933.3997730.7860.9610.315Cortex_T1CortexT1AT2G21250AT2G212500.646 46 6 5916-0.97575760.9757576
0.9053.3493060.8100.9550.252Cortex_T1CortexT1AT1G09580AT1G095800.703 17 7 33 7-0.95151520.9515152
0.8793.1668580.7580.8830.235Cortex_T1CortexT1AT5G22580AT5G225800.648 45 20 8218-0.95151520.9515152
0.9243.1158270.8480.9610.319Cortex_T1CortexT1AT5G50375CPI1 0.642 50 24 1511-0.92727270.9272727
0.9012.4535290.8020.9090.246Cortex_T1CortexT1AT3G56220AT3G562200.663 30 82 3919-0.91515150.9151515
0.9293.0225980.8580.9870.387Cortex_T1CortexT1AT5G48580FKBP15-2 0.600108 34 1220-0.91515150.9151515
0.8332.1750010.6660.7970.282Cortex_T2CortexT2AT1G17285AT1G172850.515 31 48 23 7-0.98787880.9878788
0.8662.4431800.7320.8480.254Cortex_T2CortexT2AT5G14020AT5G140200.594 7 28 3 1-0.97575760.9757576
0.8422.0960640.6840.8540.315Cortex_T2CortexT2AT5G67070RALFL34 0.539 20 59 14 6-0.95151520.9515152
0.8342.2522010.6680.8630.377Cortex_T2CortexT2AT1G64900CYP89A2 0.486 54 41 21 8-0.92727270.9272727
0.8402.7741220.6800.8870.433Cortex_T2CortexT2AT3G19820DIM 0.454102 8 1512-0.92727270.9272727
0.8302.4604560.6600.7700.233Cortex_T2CortexT2AT3G60530GATA4 0.537 21 25 26 4-0.85454550.8545455
0.8372.2686210.6740.8270.279Cortex_T2CortexT2AT5G35740AT5G357400.548 18 40 19 5-0.84242420.8424242
0.8082.5697620.6160.8250.368Cortex_T2CortexT2AT1G21750PDIL1-1 0.457 98 15 5220-0.83030300.8303030
0.8502.4569990.7000.8780.339Cortex_T2CortexT2AT5G66590AT5G665900.539 19 26 7 2-0.80606060.8060606
0.8381.8031680.6760.8950.332Cortex_T2CortexT2AT5G02000AT5G020000.563 10108 1714-0.40606060.4060606
0.8772.0521810.7540.9310.286Cortex_T3CortexT3AT5G12940AT5G129400.645 6 23 4 2-0.78181820.7818182
0.8402.0548010.6800.8910.318Cortex_T3CortexT3AT1G70280AT1G702800.573 24 22 31 8-0.76969700.7696970
0.8342.2458020.6680.9070.406Cortex_T3CortexT3AT4G03190GRH1 0.501 74 6 4313-0.73333330.7333333
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
0.8261.3004250.6520.8880.352Cortex_T7CortexT7AT5G60660PIP2-4 0.53615 68 11130.96363640.9636364
0.8531.5952890.7060.8040.245Cortex_T7CortexT7AT3G01190PER27 0.559 8 29 2 20.95151520.9515152
0.7841.9178200.5680.6090.192Cortex_T7CortexT7AT1G66725MIR163 0.41766 7 39170.95151520.9515152
0.8221.1425790.6440.9030.352Cortex_T7CortexT7AT3G14690CYP72A15 0.55110 99 14200.95151520.9515152
0.7791.6199070.5580.6670.193Cortex_T7CortexT7AT4G00330CRCK2 0.47431 26 44150.92727270.9272727
0.8541.4687300.7080.9440.335Cortex_T7CortexT7AT1G62510AT1G625100.609 3 45 1 40.91515150.9151515
0.8092.0416620.6180.6980.223Cortex_T7CortexT7AT2G39110AT2G391100.47530 2 21 50.91515150.9151515
0.8221.4313370.6440.8200.318Cortex_T7CortexT7AT2G34500CYP710A1 0.50225 51 13110.91515150.9151515
0.8552.2418900.7100.8640.327Cortex_T8CortexT8AT4G37010CEN2 0.53743 19 22 70.98787880.9878788
0.8721.9760960.7440.7790.176Cortex_T8CortexT8AT5G63600FLS5 0.60319 76 13110.98787880.9878788
0.8971.8730640.7940.9940.348Cortex_T8CortexT8AT3G57020SSL9 0.646 9114 5150.98787880.9878788
0.8681.9682750.7360.9140.363Cortex_T8CortexT8AT4G23400PIP1-5 0.55135 79 14160.98787880.9878788
0.9242.0575100.8480.9550.173Cortex_T8CortexT8AT5G15180PER56 0.782 1 55 1 30.97575760.9757576
0.9042.0911830.8080.7900.106Cortex_T8CortexT8AT5G64100PER69 0.684 3 52 2 40.97575760.9757576
0.8512.3624290.7020.7540.189Cortex_T8CortexT8AT2G31570GPX2 0.56532 12 24 50.97575760.9757576
0.8412.0951640.6820.7810.210Cortex_T8CortexT8AT1G47480CXE2 0.57129 50 35120.97575760.9757576
0.8611.9379810.7220.8770.246Cortex_T8CortexT8AT4G02700SULTR3;2 0.63114 91 19140.97575760.9757576
0.8781.8792110.7560.9150.270Cortex_T8CortexT8AT5G19240AT5G192400.64510112 10190.97575760.9757576
0.7752.1120770.5500.6780.197Cortex_T8CortexT8AT4G09760AT4G097600.48183 44120560.73333330.7333333
0.9313.0832620.8620.9350.172Cortex_T9CortexT9AT5G44480DUR 0.763 4 15 6 10.98787880.9878788
0.9312.6396330.8620.9590.183Cortex_T9CortexT9AT5G44380AT5G443800.776 3 69 7 70.98787880.9878788
0.9372.6144550.8740.9580.196Cortex_T9CortexT9AT4G17340TIP2-2 0.762 5 75 3 80.98787880.9878788
0.8852.7608090.7700.8020.094Cortex_T9CortexT9AT5G49760AT5G497600.708 8 51 25 90.98787880.9878788
0.8603.1531480.7200.7880.140Cortex_T9CortexT9AT2G43050PME16 0.64826 5 54100.98787880.9878788
0.8892.8225210.7780.8690.238Cortex_T9CortexT9AT3G51730AT3G517300.63135 37 23120.98787880.9878788
0.8773.5501760.7540.9090.321Cortex_T9CortexT9AT3G48890MSBP2 0.58857 1 39130.98787880.9878788
0.8723.0860080.7440.8350.219Cortex_T9CortexT9AT4G15610AT4G156100.61641 14 43140.98787880.9878788
0.8542.8574610.7080.8390.131Cortex_T9CortexT9AT2G27920SCPL51 0.708 9 31 60150.98787880.9878788
0.8662.8490580.7320.8480.183Cortex_T9CortexT9AT3G18170AT3G181700.66521 34 46170.98787880.9878788
0.9312.5667510.8620.9820.304Cortex_T9CortexT9AT5G53370ATPMEPCRF0.67818 84 8180.98787880.9878788
A tibble: 92 × 17
myAUCavg_diffpowerpct.1pct.2clustercelltypestagegeneNamepct.diffpct.diff_rankavg_diff_rankmyAUC_rankcombined_ranklong_corabs_long_cor
<dbl><dbl><dbl><dbl><dbl><fct><chr><fct><chr><chr><dbl><int><int><int><int><dbl><dbl>
0.9753.9866480.9501.0000.268Cortex_T0CortexT0AT3G18000NMT1 0.732 55 36 5 7-0.97575760.9757576
0.9432.7802270.8861.0000.237Cortex_T0CortexT0AT1G68560XYL1 0.763 37132 4320-0.97575760.9757576
0.9744.5380780.9481.0000.265Cortex_T0CortexT0AT2G24280AT2G242800.735 52 7 7 3-0.92727270.9272727
0.9533.2191540.9061.0000.248Cortex_T0CortexT0AT3G09455OST4C 0.752 43 95 2612-0.91515150.9151515
0.9484.5378720.8961.0000.333Cortex_T0CortexT0AT3G52960PRXIIE 0.667125 8 3011-0.90303030.9030303
0.9194.3234960.8381.0000.304Cortex_T0CortexT0AT3G54960PDIL1-3 0.696 87 1610219-0.90303030.9030303
0.9314.1301250.8620.9090.215Cortex_T0CortexT0AT2G34020AT2G340200.694 88 28 6717-0.89090910.8909091
0.9803.9391010.9601.0000.217Cortex_T0CortexT0AT2G20515AT2G205150.783 27 38 2 5-0.87878790.8787879
0.9793.6179220.9581.0000.219Cortex_T0CortexT0AT2G45050GATA2 0.781 30 61 3 6-0.87878790.8787879
0.9333.7594400.8661.0000.283Cortex_T0CortexT0AT5G05890UGT76C5 0.717 63 50 6015-0.87878790.8787879
0.9233.0227780.8460.9550.306Cortex_T1CortexT1AT4G34200PGDH1 0.649 44 33 1614-0.97575760.9757576
0.8933.3997730.7860.9610.315Cortex_T1CortexT1AT2G21250AT2G212500.646 46 6 5916-0.97575760.9757576
0.9053.3493060.8100.9550.252Cortex_T1CortexT1AT1G09580AT1G095800.703 17 7 33 7-0.95151520.9515152
0.8793.1668580.7580.8830.235Cortex_T1CortexT1AT5G22580AT5G225800.648 45 20 8218-0.95151520.9515152
0.9243.1158270.8480.9610.319Cortex_T1CortexT1AT5G50375CPI1 0.642 50 24 1511-0.92727270.9272727
0.9012.4535290.8020.9090.246Cortex_T1CortexT1AT3G56220AT3G562200.663 30 82 3919-0.91515150.9151515
0.9293.0225980.8580.9870.387Cortex_T1CortexT1AT5G48580FKBP15-2 0.600108 34 1220-0.91515150.9151515
0.8332.1750010.6660.7970.282Cortex_T2CortexT2AT1G17285AT1G172850.515 31 48 23 7-0.98787880.9878788
0.8662.4431800.7320.8480.254Cortex_T2CortexT2AT5G14020AT5G140200.594 7 28 3 1-0.97575760.9757576
0.8422.0960640.6840.8540.315Cortex_T2CortexT2AT5G67070RALFL34 0.539 20 59 14 6-0.95151520.9515152
0.8342.2522010.6680.8630.377Cortex_T2CortexT2AT1G64900CYP89A2 0.486 54 41 21 8-0.92727270.9272727
0.8402.7741220.6800.8870.433Cortex_T2CortexT2AT3G19820DIM 0.454102 8 1512-0.92727270.9272727
0.8302.4604560.6600.7700.233Cortex_T2CortexT2AT3G60530GATA4 0.537 21 25 26 4-0.85454550.8545455
0.8372.2686210.6740.8270.279Cortex_T2CortexT2AT5G35740AT5G357400.548 18 40 19 5-0.84242420.8424242
0.8082.5697620.6160.8250.368Cortex_T2CortexT2AT1G21750PDIL1-1 0.457 98 15 5220-0.83030300.8303030
0.8502.4569990.7000.8780.339Cortex_T2CortexT2AT5G66590AT5G665900.539 19 26 7 2-0.80606060.8060606
0.8381.8031680.6760.8950.332Cortex_T2CortexT2AT5G02000AT5G020000.563 10108 1714-0.40606060.4060606
0.8772.0521810.7540.9310.286Cortex_T3CortexT3AT5G12940AT5G129400.645 6 23 4 2-0.78181820.7818182
0.8402.0548010.6800.8910.318Cortex_T3CortexT3AT1G70280AT1G702800.573 24 22 31 8-0.76969700.7696970
0.8342.2458020.6680.9070.406Cortex_T3CortexT3AT4G03190GRH1 0.501 74 6 4313-0.73333330.7333333
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
0.8261.3004250.6520.8880.352Cortex_T7CortexT7AT5G60660PIP2-4 0.53615 68 11130.96363640.9636364
0.8531.5952890.7060.8040.245Cortex_T7CortexT7AT3G01190PER27 0.559 8 29 2 20.95151520.9515152
0.7841.9178200.5680.6090.192Cortex_T7CortexT7AT1G66725MIR163 0.41766 7 39170.95151520.9515152
0.8221.1425790.6440.9030.352Cortex_T7CortexT7AT3G14690CYP72A15 0.55110 99 14200.95151520.9515152
0.7791.6199070.5580.6670.193Cortex_T7CortexT7AT4G00330CRCK2 0.47431 26 44150.92727270.9272727
0.8541.4687300.7080.9440.335Cortex_T7CortexT7AT1G62510AT1G625100.609 3 45 1 40.91515150.9151515
0.8092.0416620.6180.6980.223Cortex_T7CortexT7AT2G39110AT2G391100.47530 2 21 50.91515150.9151515
0.8221.4313370.6440.8200.318Cortex_T7CortexT7AT2G34500CYP710A1 0.50225 51 13110.91515150.9151515
0.8552.2418900.7100.8640.327Cortex_T8CortexT8AT4G37010CEN2 0.53743 19 22 70.98787880.9878788
0.8721.9760960.7440.7790.176Cortex_T8CortexT8AT5G63600FLS5 0.60319 76 13110.98787880.9878788
0.8971.8730640.7940.9940.348Cortex_T8CortexT8AT3G57020SSL9 0.646 9114 5150.98787880.9878788
0.8681.9682750.7360.9140.363Cortex_T8CortexT8AT4G23400PIP1-5 0.55135 79 14160.98787880.9878788
0.9242.0575100.8480.9550.173Cortex_T8CortexT8AT5G15180PER56 0.782 1 55 1 30.97575760.9757576
0.9042.0911830.8080.7900.106Cortex_T8CortexT8AT5G64100PER69 0.684 3 52 2 40.97575760.9757576
0.8512.3624290.7020.7540.189Cortex_T8CortexT8AT2G31570GPX2 0.56532 12 24 50.97575760.9757576
0.8412.0951640.6820.7810.210Cortex_T8CortexT8AT1G47480CXE2 0.57129 50 35120.97575760.9757576
0.8611.9379810.7220.8770.246Cortex_T8CortexT8AT4G02700SULTR3;2 0.63114 91 19140.97575760.9757576
0.8781.8792110.7560.9150.270Cortex_T8CortexT8AT5G19240AT5G192400.64510112 10190.97575760.9757576
0.7752.1120770.5500.6780.197Cortex_T8CortexT8AT4G09760AT4G097600.48183 44120560.73333330.7333333
0.9313.0832620.8620.9350.172Cortex_T9CortexT9AT5G44480DUR 0.763 4 15 6 10.98787880.9878788
0.9312.6396330.8620.9590.183Cortex_T9CortexT9AT5G44380AT5G443800.776 3 69 7 70.98787880.9878788
0.9372.6144550.8740.9580.196Cortex_T9CortexT9AT4G17340TIP2-2 0.762 5 75 3 80.98787880.9878788
0.8852.7608090.7700.8020.094Cortex_T9CortexT9AT5G49760AT5G497600.708 8 51 25 90.98787880.9878788
0.8603.1531480.7200.7880.140Cortex_T9CortexT9AT2G43050PME16 0.64826 5 54100.98787880.9878788
0.8892.8225210.7780.8690.238Cortex_T9CortexT9AT3G51730AT3G517300.63135 37 23120.98787880.9878788
0.8773.5501760.7540.9090.321Cortex_T9CortexT9AT3G48890MSBP2 0.58857 1 39130.98787880.9878788
0.8723.0860080.7440.8350.219Cortex_T9CortexT9AT4G15610AT4G156100.61641 14 43140.98787880.9878788
0.8542.8574610.7080.8390.131Cortex_T9CortexT9AT2G27920SCPL51 0.708 9 31 60150.98787880.9878788
0.8662.8490580.7320.8480.183Cortex_T9CortexT9AT3G18170AT3G181700.66521 34 46170.98787880.9878788
0.9312.5667510.8620.9820.304Cortex_T9CortexT9AT5G53370ATPMEPCRF0.67818 84 8180.98787880.9878788
In [36]:
# subset for endodermis
Endo_QC  <- subset(rc.integrated, celltype.anno %in% c("Quiescent Center", "Stem Cell Niche", "Endodermis"))
In [37]:
# pseudobulk of each stage of endodermis


  afm <- as.matrix(Endo_QC@assays$integrated@data)
  pooled_endo <- matrix(nrow=nrow(afm), ncol = 0)

  for (i in unique(Endo_QC@meta.data$consensus.time.group)) {
    m <- afm[,which(Endo_QC@meta.data$consensus.time.group==i)]
    pooled_endo <- cbind(pooled_endo, rowSums(m)/ncol(m))
  }
In [38]:
colnames(pooled_endo) <- unique(Endo_QC@meta.data$consensus.time.group)
In [39]:
(endo_sub_m <- pooled_endo[genes_to_plt_endo, c("T0", "T1", "T2", "T3", "T4", "T5", "T6", "T7", "T8", "T9")])
A matrix: 91 × 10 of type dbl
T0T1T2T3T4T5T6T7T8T9
AT5G06200-0.3858394-0.39678862-0.46835782-0.46389492-0.48952583-0.345707311 0.373693602.56673485.0107853 9.13966786
AT4G02090-0.3510602-0.39418992-0.38203199-0.42705875-0.47492429-0.338104301 0.099410931.61658624.1614898 8.15244850
AT3G11550-0.4953639-0.46677592-0.55521079-0.55042610-0.57283147-0.212771666 1.132449694.46946997.1406041 9.23916043
AT2G39430-0.4245708-0.42423281-0.49306885-0.50669223-0.53260985-0.322867113 0.638631143.36315705.6825499 9.55358915
AT5G65020-0.7931151-0.78948578-0.32828818-0.31230876-0.28326219-0.018500135 0.558664511.74598472.0873044 3.96941827
AT1G44970-0.2927576-0.31995370-0.26834816-0.17713949-0.18024182-0.005917786 0.680628132.50765693.9772031 8.65848367
AT2G28671-0.3952874-0.36704201-0.34483836-0.38320250-0.27876517 0.370441468 1.463674434.51838936.2175832 8.02013733
AT2G27370-0.4853259-0.47650478-0.11488260-0.07551403 0.19024764 0.546233617 1.306345152.40881353.8982847 9.42446012
AT1G80640-0.1877883-0.16877722 0.28451788 0.47826073 0.90848674 1.246292123 1.487155411.92505632.3893394 5.39931515
AT5G44790-0.7297605-0.82119706-0.73355996-0.66759998-0.61724440-0.413716085-0.122520910.65390691.0491486 4.40288214
AT4G34050-2.3566686-1.79585068-0.47006990-0.19208806 0.33437955 0.864863010 1.918478761.86602911.4778715 8.44184172
AT3G24020-0.4565064-0.46963364-0.49882608-0.48197478-0.58610801-0.225118901 1.066134435.23682917.3932050 8.67938388
AT2G36100-0.6362937-0.79475947-0.93885657-0.94604437-0.83456318 0.228821138 3.807453818.60283709.6804359 9.96958029
AT4G37790-0.7497711-0.78208364-0.60697921-0.65552796-0.13524968 1.335909783 2.388845272.90372083.2024336-0.03346066
AT1G03850-1.2318407-1.25238601-1.30197303-1.40730057-1.44907875-0.968284819 0.181711603.18609624.4174776-0.42461356
AT4G13580-0.5008437-0.57179321-0.54536763-0.50029677-0.54254935-0.097553557 1.425156575.89594847.8413739 8.88636063
AT3G14280-0.8995567-0.89017261-0.90888272-0.93598856-0.88945990-0.381396618 0.475648092.43306863.4801323 0.92595224
AT3G03520-0.6705847-0.64697454-0.56757028-0.57162980-0.46632306-0.025015680 0.889153271.63410592.2816040 2.88187244
AT4G34230-1.2553710-1.31421382-1.14593246-1.04647799-0.24564486 1.600367913 2.941426742.68097702.2597036 0.83248303
AT2G15340-0.2702526-0.19758729 0.12811957 0.21321145 0.75248923 1.848805135 3.008392783.69978993.2691498 0.26131738
AT4G21620-0.0665043-0.08954689-0.05389634-0.06946120-0.07707639-0.010859435 0.133442660.51650650.6120284 0.14236826
AT5G08240-0.2973488-0.30150312-0.24060980-0.23140286-0.10279308 0.549758485 1.290668362.51594963.0404608 0.25374680
AT4G23493-0.1952053-0.11056667 0.20417212 0.24115336 0.37262324 0.548054281 0.909304541.47015601.4258435 0.83612281
AT3G09270-0.5222286-0.74368338-0.41671205 0.12180841 0.97123311 1.696658969 2.891366893.45899603.7708344 2.41603978
AT5G66390-0.7865327-0.81235975-0.66928313-0.66204141-0.27166507 1.859449235 5.506724278.52048259.2636907 9.82708009
AT5G42030-0.5148810-0.57885907-0.36773895-0.07448089 0.35071522 1.000217246 1.944576873.48635394.2439833 3.46502993
AT5G21090-0.6161858-0.67349504-0.37545738-0.30014663-0.15925541 0.094704127 0.657172001.45888601.5197383 2.44148386
AT3G06125-0.6865119-0.50920953 0.06696524 0.46182362 0.85619254 1.632832552 3.093233644.82479085.8022508 4.20825418
AT5G58375-0.7616663-0.59264250 0.17898453 0.34310756 0.55534713 1.018531430 1.892987343.19642803.5951370 3.81192220
AT5G25770-0.6668781-0.44818145 0.84302848 1.17726542 1.96982016 2.722521276 2.872635432.25931991.9620198 0.54683602
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
AT5G45200-0.094812531.5024485 4.16412767 3.330874408 2.27020679 0.93818090 0.17408611-0.08281854-0.1233102-0.14467148
AT3G54220 0.159976741.7797207 4.10576610 3.042229683 2.06278965 1.08856670 0.79181199 0.68605627 0.9144775 0.24598116
AT1G13280 0.268800041.0193887 2.85291382 2.291647806 1.48050830 0.31571107-0.24602163-0.48943928-0.5440299-0.35301278
AT5G63660 0.361959262.4154075 7.35964308 6.588208050 4.71766222 1.80170410 0.33312001-0.27383094-0.4750205-0.47575585
AT3G23730-0.350378872.5205206 6.61675109 5.173271016 2.37608381 0.15530188-0.36696652-0.46145889-0.5075597-0.44366898
AT1G65900-0.013023720.9910938 3.28294073 2.006943204 0.77087526-0.07406016-0.13684295-0.16673843-0.1641097-0.14115060
AT4G17970-0.028949931.2838070 3.27922272 2.098688152 1.06279669 0.22177797-0.02693181-0.14866521-0.1750839-0.18095174
AT4G39900 0.484999541.0967093 2.27547823 1.772786166 1.03698218 0.31039154-0.01100929-0.16717412-0.2689195-0.36687103
AT1G54580 1.982667503.6876237 5.06381552 3.528433605 1.52791878 0.02612911-0.48015255-0.76024700-0.8939658-0.81462618
AT5G55730 0.985387820.9087416 2.36710946 2.008857118 1.13156187 0.08978245-0.46166273-0.62392895-0.6873813-0.64471899
AT4G16265 1.266904151.8487911 2.33976340 1.759547253 1.40929677 0.85242534 0.45293427-0.07504988-0.1788466-0.20528832
AT3G54560 2.140117183.7699787 2.93119285-0.458639358-0.75691234-0.87250212-0.90766414-0.76493873-0.7761217-0.67776268
AT3G45980 1.715338553.9356025 3.48103668-0.004002006-0.30807872-0.54395603-0.60423954-0.38036369-0.4132243-0.51589831
AT5G23860-0.121362541.8044533 4.01882136 2.478177333 0.54894170-0.67886274-1.00407308-1.03882213-1.0139854-1.00233933
AT5G28640 1.182162012.7326165 2.21356819 0.829933580 0.11763119-0.10376573-0.05087782 0.10359300 0.1104304-0.12924947
AT5G07030 0.591395531.4820332 1.89946991 0.458414248-0.45850379-0.92097213-1.00536070-0.92542558-0.9346405-0.86130642
AT2G43360 1.234294042.2113871 1.79956254 0.971765788 0.35059114-0.05499681-0.23880023-0.29939148-0.3106121-0.13261472
AT2G22230 1.395373032.3420559 1.37626325 0.606205010 0.03332351-0.43526367-0.47140513-0.46982493-0.4716208-0.25754272
AT5G59910 1.499030902.6081995 2.45248608-0.333024403-0.54318185-0.60944705-0.54676720-0.31405661-0.3156692-0.57271143
AT1G14900 0.989335241.9633546 2.52267063 0.158046980-0.11178175-0.34593234-0.43476272-0.24899293-0.2775612-0.43776590
AT5G27670 0.662351041.3058391 1.74244736-0.475760718-0.56068768-0.58008121-0.66700418-0.53594857-0.5005500-0.57728421
AT3G05190 0.075190550.6095619 1.34615564 0.842070584 0.41259388 0.01469875-0.08129877-0.12531373-0.1425462-0.06831040
AT1G46264 0.560767691.5893468 1.64617315 0.813740282 0.20641275-0.13377767-0.16802978-0.15319769-0.1450894-0.11332716
AT4G21960 1.403922571.3224318-1.46562244-1.802413385-2.03752283-2.13481348-2.10916590-1.98248425-1.9587413-1.77702963
AT3G60530 0.633043011.7916014 1.29997529 0.254452569-0.48838273-0.67251493-0.66828068-0.63286692-0.6602692-0.54121124
AT2G45050 2.276948242.0452319 0.06479671-0.894146186-1.38110545-1.44507881-1.40493528-1.28920366-1.2623128-1.09599731
AT2G20515 3.212852952.7231023 0.16024779-0.342897328-0.65667461-0.71266069-0.69551494-0.60233201-0.6229676-0.53078236
AT5G48660 0.383662570.7355706 0.52066704 0.259425941 0.04593673-0.11679337-0.15231925-0.13947433-0.1311098-0.10041897
AT3G52960 1.535445562.2557511 1.09291420 0.449842604-0.06399537-0.36859733-0.51628803-0.44412095-0.4756124-0.37910350
AT3G56220 0.574377111.7519554 2.18585643 0.961780354 0.33810594-0.04135639-0.05803634-0.10004737-0.1296028-0.08310022
In [40]:
# quantile normalize

mat = endo_sub_m
mat2 = t(apply(mat, 1, function(x) {
    q10 = quantile(x, 0.1)
    q90 = quantile(x, 0.9)
    x[x < q10] = q10
    x[x > q90] = q90
    scale(x)
}))
colnames(mat2) = colnames(mat)
In [41]:
# side annotation with color to match umap

endo_sa = rowAnnotation(foo = anno_block(gp = gpar(fill = "#0000FF"),
        labels = c("Endodermis"), 
        labels_gp = gpar(col = "white", fontsize = 18)))
In [42]:
# genes to mark on side of heatmap

(endo_mat_for_mark <- data.frame(mat2) %>% 
 rownames_to_column("gene") %>%
 left_join(feature_names) %>% 
 mutate(index=dplyr::row_number()) %>%
select(Name, index) %>%
filter(Name %in% c("XTH16", "SCR", "MYB36", "CASP1", "CASP2", "CASP3", "CASP4")))
Joining, by = "gene"

A data.frame: 7 × 2
Nameindex
<chr><int>
CASP4 1
CASP2 3
CASP3 8
CASP113
MYB3638
SCR 63
XTH1666
In [43]:
# at - index of genes you want to mark
# labels - Names to show

endo_mark_anno = rowAnnotation(foo = anno_mark(at = endo_mat_for_mark$index, labels = endo_mat_for_mark$Name, labels_gp = gpar(col = "black", fontsize = 16)))
In [44]:
Endo_hm <- Heatmap(mat2, 
        col = colorRamp2(c(-1.5, 0, 1.5), 
                         c('blue','white','red')), 
                show_column_names = T,
                cluster_columns = F,
        cluster_rows=F,
                show_row_names = F, 
                   left_annotation=endo_sa,
                   right_annotation=endo_mark_anno,
               heatmap_legend_param = list(title_position="topcenter", title = "Expression", direction="horizontal"))

draw(Endo_hm, padding = unit(c(10, 10, 10, 10), "mm"), heatmap_legend_side = "top")
In [45]:
# cortex subset
Cor_QC  <- subset(rc.integrated, celltype.anno %in% c("Quiescent Center", "Stem Cell Niche", "Cortex"))
In [46]:
# pseudobulk of each stage of cortex


  afm <- as.matrix(Cor_QC@assays$integrated@data)
  pooled_cortex <- matrix(nrow=nrow(afm), ncol = 0)

  for (i in unique(Cor_QC@meta.data$consensus.time.group)) {
    m <- afm[,which(Cor_QC@meta.data$consensus.time.group==i)]
    pooled_cortex <- cbind(pooled_cortex, rowSums(m)/ncol(m))
  }
In [47]:
colnames(pooled_cortex) <- unique(Cor_QC@meta.data$consensus.time.group)
In [48]:
(cor_sub_m <- pooled_cortex[genes_to_plt_cortex, c("T0", "T1", "T2", "T3", "T4", "T5", "T6", "T7", "T8", "T9")])
A matrix: 91 × 10 of type dbl
T0T1T2T3T4T5T6T7T8T9
AT3G18000 1.45629812.11251681.59977580.65169036 0.374898469-0.003056075-0.329839439-0.4234481576-0.50563275-0.50465766
AT1G68560 4.47875063.07393191.06525790.59338684 0.413303183-0.049724141-0.510957065-0.5707978573-0.67843653-0.71734785
AT2G24280 0.20428161.20571381.46894250.88514567 0.651492691 0.322703214 0.111291149 0.0201927247-0.05571095-0.08492299
AT3G09455 0.71826881.25545370.53972600.04164711-0.116544105-0.342577959-0.554016622-0.6115180633-0.62231791-0.64423775
AT3G52960 1.53907272.38812001.04583930.22814958 0.009500409-0.211504782-0.515399392-0.5318340513-0.60524844-0.61847596
AT3G54960 0.39110530.41952761.38323261.04689142 0.789351731 0.435904820-0.231103881-0.4848968987-0.65688367-0.67196142
AT2G34020 1.82899051.89941371.40749080.44504486 0.176480032-0.181292723-0.416326059-0.4290655006-0.44923508-0.45983640
AT2G20515 3.22310883.73410101.80976340.34749119-0.034612938-0.445260471-0.639877986-0.6416044804-0.64038185-0.64257177
AT2G45050 2.29168363.14132382.75922120.61320732 0.068032506-0.857371093-1.318017014-1.2871482382-1.35886754-1.36283422
AT5G05890 0.74111261.25630291.02613780.58056746 0.409808851 0.059864860-0.227912941-0.2462220700-0.26645606-0.26545003
AT4G34200 1.25783531.85474711.29523110.72060000 0.434128960 0.094309488-0.526250516-0.7076745100-0.90608364-0.98431235
AT2G21250 0.86820941.47872690.77936950.33277870 0.231478882 0.010877723-0.208840556-0.3855056251-0.51138725-0.56446705
AT1G09580 0.24321410.76711100.81357350.47703912 0.305800011 0.017599501-0.305777982-0.4180947460-0.48130615-0.49554806
AT5G22580 0.35728061.24605360.94983410.42652659 0.306227444 0.235021899 0.023086314-0.0392016839-0.09315772-0.10346438
AT5G50375 1.32890302.36827481.94698461.07165610 0.786521380 0.231828266-0.371225396-0.4855661367-0.53842904-0.54221664
AT3G56220 0.57867881.81679061.18758090.33771613 0.161842761 0.033954885-0.112077587-0.1195884118-0.14183495-0.15671290
AT5G48580 0.89476071.96983522.03389741.54457345 1.132927316 0.609677209-0.521814316-0.9220307072-1.29577248-1.37828877
AT1G17285 1.83836402.66028422.58202681.25017813 0.902320870 0.273571769-0.263060973-0.4623119022-0.75052265-0.79361576
AT5G14020 0.76344151.04246732.23494591.63653425 1.368368296 0.814412194-0.092144119-0.5120790514-0.80573170-0.85081814
AT5G67070 0.74603540.95453392.12934701.80099285 1.596632300 1.163589444 0.166611133-0.2132324163-0.51216865-0.56319274
AT1G64900 0.71300322.16086972.89276482.17706666 1.953479232 1.185478795 0.430668448 0.2666562114 0.06737647 0.04956654
AT3G19820 1.31337482.69440703.85289092.49157054 1.951764426 1.081909785-0.154132000-0.5637510564-0.90155171-0.94439512
AT3G60530 0.64923272.56221193.18225601.43130204 0.931737454 0.054155789-0.537388749-0.5977077868-0.61725570-0.60245794
AT5G35740 0.62106101.37465074.05896173.31610139 2.434884710 1.474137669 0.003824548-0.3222311457-0.60381767-0.69735277
AT1G21750 0.21767700.87412912.13059971.49084877 1.164035768 0.521270421-0.330455189-0.5377306545-0.74118485-0.71060408
AT5G66590 0.38152561.93125323.63278302.76766004 2.420213108 1.906832194 0.620413836 0.0028422463-0.49562324-0.58964600
AT5G02000-0.36635941.12853203.53461543.44047289 2.962183144 2.428352009 0.638198658-0.0001308083-0.35165516-0.41102049
AT5G12940 1.23208770.89287043.39198114.27726503 4.167349446 3.437580278 1.089410599-0.0940078867-0.87624082-1.03777900
AT1G70280 0.36617711.02699993.05969893.14198599 2.839073229 2.197381589 0.744949921 0.1863841017-0.23896929-0.30647609
AT4G03190 0.29368540.93642303.35227133.79672850 3.453015156 2.791831564 0.662759976-0.2789994430-0.87774736-0.97830939
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
AT1G13930-1.34439814-1.24318847-0.362714271 0.18743191 0.7804779 2.07667936 5.586901356.424534017.76967037.4227910
AT5G60660-1.60946952-1.53321725-0.165286365 0.71116914 1.4491902 1.91610217 2.950667114.133787954.43994625.5218999
AT3G01190-2.14310665-2.10293274-0.850661305-0.09404766 0.6804595 1.32325265 3.547075954.657169964.50878995.0916518
AT1G66725-0.86153200-0.84108351-0.641141255-0.52232552-0.4164361-0.03121431 1.239473881.616242221.94541011.6615306
AT3G14690-0.76544548-0.76214837 0.159928914 0.44840504 0.6226270 0.80850026 2.083156493.710827084.70130015.5741630
AT4G00330-0.53802052-0.57797100-0.492804074-0.39979331-0.3386538-0.21579669 0.565621161.018679921.51278261.1321281
AT2G39110-0.65418419-0.59779467-0.252061715-0.28854470-0.1966902-0.04370181 0.715981590.841232110.73194510.6763377
AT2G34500-1.37885076-1.33726846-0.471559210 0.17799980 0.7357248 1.21289159 3.569463594.315621373.93240524.5452128
AT4G37010-1.17838814-1.09931084-0.197258517 0.08263832 0.1385279 0.44394853 1.579759412.861839835.24373685.5496765
AT5G63600-1.46831718-1.37438446-0.929264150-0.86914562-0.7610614-0.39113386 1.090685482.100107172.95160763.1101740
AT3G57020-1.36634336-1.31987855 0.393026913 1.68484065 2.3100253 2.60635116 3.953099285.468856387.01133748.2339565
AT4G23400-1.70615084-1.70766325-0.830079621 0.03173150 0.6838932 1.34631921 3.418336405.259437456.62547568.0719090
AT5G15180-1.12361852-1.14517530-0.875267441-0.81117368-0.6915080-0.53899142 0.979323053.741487007.30933819.0931693
AT5G64100-2.26660195-2.24833884-2.136235952-2.12521916-2.0752466-2.03784264-1.402592720.422956504.83042347.2566697
AT2G31570-1.07351266-1.12706740-0.773450073-0.67693510-0.6763332-0.48356235-0.036580550.730216872.72682413.3040087
AT1G47480-0.64819059-0.72291597-0.315905773-0.11735721-0.0247899 0.16582862 0.916169431.516468302.33731752.5760710
AT4G02700-0.29910410-0.32640221 0.007837718 0.01921877 0.0912477 0.16037958 0.538936381.398658272.15295912.6115989
AT5G19240-0.91065722-1.01545920-0.801134604-0.77651091-0.6862805-0.58006298 0.598449432.453390735.96407817.6302250
AT4G09760-0.06271868-0.05907882-0.275104369-0.24630742-0.2520515-0.14603260 0.485905811.648858673.29821333.6686387
AT5G44480-0.63419551-0.65124105-0.565513115-0.53403700-0.5054886-0.45398743-0.031976030.727293742.24310233.3977857
AT5G44380-2.05053479-2.01778433-1.492034250-1.14733951-0.7285131-0.58064933 0.854409112.600321253.87429225.2728315
AT4G17340-1.72964851-1.71563588-1.561866232-1.57047345-1.4031728-1.33780702-0.275816291.762136835.35499817.6873168
AT5G49760-0.51732096-0.54572306-0.435694449-0.45147613-0.4578218-0.39668672-0.316127690.056268080.83253161.5094385
AT2G43050-0.62801972-0.63341185-0.585217330-0.60738140-0.6038916-0.54736117-0.281659450.211793271.85434843.2121493
AT3G51730-0.83410780-0.87195840-0.520902437-0.31879050-0.1702752-0.11411525 0.116207040.560925861.12560051.7320060
AT3G48890-0.42156266-0.49588032-0.195237803-0.04832097 0.1280329 0.09821212 0.497534181.194082381.84108262.9318223
AT4G15610-1.53735614-1.49278578-1.021188326-0.84381824-0.6965847-0.54446844 0.344624151.366969402.53542413.6972668
AT2G27920-0.35629574-0.38667154-0.317943310-0.24455367-0.2388745-0.25448165 0.114015770.918796532.73652704.2608756
AT3G18170-0.45648222-0.52115158-0.275310207-0.24616764-0.2391673-0.20533357 0.227822931.100840572.96644324.4635864
AT5G53370-0.58586651-0.58034723 0.004945644 0.46198472 0.8182913 1.07268713 2.760272574.170175255.87832247.4511394
In [49]:
# annotation bar for consensus time

col_fun <- brewer.pal(10,"Spectral")

names(col_fun) <- c("T0", "T1", "T2", "T3", "T4", "T5", "T6", "T7", "T8", "T9")
col_fun

ha = HeatmapAnnotation(`Consensus Time Group` = c("T0", "T1", "T2", "T3", "T4", "T5", "T6", "T7", "T8", "T9"), col = list(`Consensus Time Group` = col_fun), show_legend = F)
T0
'#9E0142'
T1
'#D53E4F'
T2
'#F46D43'
T3
'#FDAE61'
T4
'#FEE08B'
T5
'#E6F598'
T6
'#ABDDA4'
T7
'#66C2A5'
T8
'#3288BD'
T9
'#5E4FA2'
In [50]:
# quantile normalize

mat = cor_sub_m
mat2 = t(apply(mat, 1, function(x) {
    q10 = quantile(x, 0.1)
    q90 = quantile(x, 0.9)
    x[x < q10] = q10
    x[x > q90] = q90
    scale(x)
}))
colnames(mat2) = colnames(mat)
In [51]:
# side annotation to match cortex colors

cortex_sa = rowAnnotation(foo = anno_block(gp = gpar(fill = "#82B6FF"),
        labels = c("Cortex"), 
        labels_gp = gpar(col = "black", fontsize = 18)))
In [52]:
# genes to label on side of heatmap for cortex

(cor_mat_for_mark <- data.frame(mat2) %>% 
 rownames_to_column("gene") %>%
 left_join(feature_names) %>% 
 mutate(index=dplyr::row_number()) %>%
select(Name, index) %>%
filter(Name %in% c("AT1G17285", "AT5G02000", "C/VIF2", "CYP72A15", "PER56", "PER69", "AT4G09760")))
Joining, by = "gene"

A data.frame: 7 × 2
Nameindex
<chr><int>
AT1G1728518
AT5G0200027
C/VIF2 60
CYP72A15 66
PER56 74
PER69 75
AT4G0976080
In [53]:
# at - index of genes you want to mark
# labels - Names to show

cor_mark_anno = rowAnnotation(foo = anno_mark(at = cor_mat_for_mark$index, labels = cor_mat_for_mark$Name, labels_gp = gpar(col = "black", fontsize = 16)))
In [54]:
# ['#d8b365','#f5f5f5','#5ab4ac']

Cortex_hm <- Heatmap(mat2, 
        col = colorRamp2(c(-1.5, 0, 1.5), 
                         c('blue','white','red')), 
                show_column_names = F,
                cluster_columns = F,
        cluster_rows=F,
                show_row_names = F, 
        show_heatmap_legend = F, 
        left_annotation=cortex_sa, 
        right_annotation=cor_mark_anno)

Cortex_hm
In [55]:
# text annotation for consensus time

ht = columnAnnotation(foo = anno_text(c("T0", "T1", "T2", "T3", "T4", "T5", "T6", "T7", "T8", "T9"), rot = 0, just = "center", gp = gpar(fontsize = 16)))
In [56]:
options(repr.plot.width=12, repr.plot.height=10)


ht_list = ht %v% ha %v% Endo_hm %v% Cortex_hm

draw(ht_list, ht_gap = unit(0.2, "cm"), padding = unit(c(2, 2, 5, 10), "mm"), heatmap_legend_side = "bottom")
# grab heatmap as a grob to arrange in cowplot
hm_final <- grid.grabExpr(draw(ht_list, ht_gap = unit(0.2, "cm"), padding = unit(c(2, 2, 5, 10), "mm"), heatmap_legend_side = "bottom"))## bottom, left, top and right
In [57]:
# output heatmap as pdf

pdf("./supp_data/WT_Ground_tissue_heatmap.pdf", width = 12, height = 10)
draw(ht_list, ht_gap = unit(0.2, "cm"), padding = unit(c(2, 2, 5, 10), "mm"), heatmap_legend_side = "bottom") ## bottom, left, top and right
dev.off()
png: 2
In [58]:
DefaultAssay(rc.integrated) <- "integrated"


(SCR <- FeaturePlot(rc.integrated, features = "AT3G54220",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("SCR"))
In [59]:
(MYB36 <- FeaturePlot(rc.integrated, features = "AT5G57620",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = F, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("MYB36"))
In [60]:
(CASP <- FeaturePlot(rc.integrated, features = "AT2G36100",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("CASP1"))
In [61]:
options(repr.plot.width=8, repr.plot.height=6)
(Cor_1 <- FeaturePlot(rc.integrated, features = "AT5G02000",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("AT5G02000"))
In [62]:
options(repr.plot.width=8, repr.plot.height=6)
(Cor_2 <- FeaturePlot(rc.integrated, features = "AT5G64620",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("C/VIF2"))
In [63]:
options(repr.plot.width=8, repr.plot.height=6)
(Cor_3 <- FeaturePlot(rc.integrated, features = "AT4G09760",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("AT4G09760"))
In [64]:
plot_anno <- function(rc.integrated){
order <- c("Quiescent Center", "Stem Cell Niche", "Columella", "Lateral Root Cap", "Atrichoblast", "Trichoblast", "Cortex", "Endodermis", "Pericycle", "Phloem", "Xylem", "Procambium", "Unknown")
palette <- c("#9400D3","#DCD0FF", "#5AB953", "#BFEF45", "#008080", "#21B6A8", "#82B6FF", "#0000FF","#FF9900","#E6194B", "#9A6324", "#FFE119","#EEEEEE")
rc.integrated$celltype.anno <- factor(rc.integrated$celltype.anno, levels = order[sort(match(unique(rc.integrated$celltype.anno),order))])
color <- palette[sort(match(unique(rc.integrated$celltype.anno),order))]
p1 <- DimPlot(rc.integrated, reduction = "umap", group.by = "celltype.anno", cols=color) + ggtitle("Cell Type") + theme(plot.title = element_text(hjust = 0.5))
p2 <- DimPlot(rc.integrated, reduction = "umap", group.by = "time.anno", order = c("Maturation","Elongation","Meristem"),cols = c("#DCEDC8", "#42B3D5", "#1A237E")) + ggtitle("Developmental Stage") + theme(plot.title = element_text(hjust = 0.5))
p3 <- DimPlot(rc.integrated, reduction = "umap", group.by = "consensus.time.group", cols=brewer.pal(10,"Spectral")) + ggtitle("Consensus Time") + theme(plot.title = element_text(hjust = 0.5))
options(repr.plot.width=25, repr.plot.height=8)
gl <- lapply(list(p1, p2, p3), ggplotGrob)
gwidth <- do.call(unit.pmax, lapply(gl, "[[", "widths"))
gl <- lapply(gl, "[[<-", "widths", value = gwidth)
gridExtra::grid.arrange(grobs=gl, ncol=3)
}
In [65]:
options(repr.plot.width=28, repr.plot.height=6)
top_umaps <- plot_anno(rc.integrated)
In [66]:
options(repr.plot.width=24, repr.plot.height=18)

gene_umaps <- plot_grid(SCR, MYB36, CASP, Cor_1, Cor_2, Cor_3, ncol=3, align="hv")

gene_hm <- plot_grid(gene_umaps, hm_final, ncol=2, rel_widths = c(1.6, 1))
In [67]:
options(repr.plot.width=28, repr.plot.height=18)

plot_grid(top_umaps, gene_hm, ncol=1, rel_heights = c(1, 1.5))
In [68]:
ggsave("./supp_data/Endo_cortex_traj_combined.pdf",
  width = 28,
  height = 18)
In [69]:
sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-conda_cos6-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS

Matrix products: default
BLAS/LAPACK: /home/tmnolan7/anaconda3/envs/r_3.6.1/lib/libopenblasp-r0.3.7.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ggrepel_0.8.2        circlize_0.4.8       ComplexHeatmap_2.2.0
 [4] cowplot_1.0.0        future_1.17.0        RColorBrewer_1.1-2  
 [7] Seurat_3.1.5         forcats_0.5.0        stringr_1.4.0       
[10] dplyr_0.8.5          purrr_0.3.3          readr_1.3.1         
[13] tidyr_1.0.2          tibble_3.0.1         ggplot2_3.3.0       
[16] tidyverse_1.3.0     

loaded via a namespace (and not attached):
  [1] Rtsne_0.15           colorspace_1.4-1     rjson_0.2.20        
  [4] ellipsis_0.3.0       ggridges_0.5.2       IRdisplay_0.7.0     
  [7] GlobalOptions_0.1.1  base64enc_0.1-3      fs_1.4.1            
 [10] clue_0.3-57          rstudioapi_0.11      farver_2.0.3        
 [13] leiden_0.3.3         listenv_0.8.0        npsurv_0.4-0        
 [16] fansi_0.4.1          lubridate_1.7.8      xml2_1.3.0          
 [19] codetools_0.2-16     splines_3.6.1        lsei_1.2-0          
 [22] IRkernel_1.1         jsonlite_1.6.1       broom_0.5.5         
 [25] ica_1.0-2            cluster_2.1.0        dbplyr_1.4.2        
 [28] png_0.1-7            uwot_0.1.8           sctransform_0.2.1   
 [31] compiler_3.6.1       httr_1.4.1           backports_1.1.7     
 [34] assertthat_0.2.1     Matrix_1.2-18        lazyeval_0.2.2      
 [37] cli_2.0.2            htmltools_0.4.0      tools_3.6.1         
 [40] rsvd_1.0.3           igraph_1.2.5         gtable_0.3.0        
 [43] glue_1.4.1           RANN_2.6.1           reshape2_1.4.3      
 [46] rappdirs_0.3.1       Rcpp_1.0.4.6         cellranger_1.1.0    
 [49] vctrs_0.2.4          gdata_2.18.0         ape_5.3             
 [52] nlme_3.1-145         lmtest_0.9-37        globals_0.12.5      
 [55] rvest_0.3.5          lifecycle_0.2.0      irlba_2.3.3         
 [58] gtools_3.8.2         MASS_7.3-51.5        zoo_1.8-8           
 [61] scales_1.1.1         hms_0.5.3            parallel_3.6.1      
 [64] gridExtra_2.3        reticulate_1.15      pbapply_1.4-2       
 [67] stringi_1.4.3        caTools_1.18.0       shape_1.4.4         
 [70] repr_1.1.0           rlang_0.4.6          pkgconfig_2.0.3     
 [73] bitops_1.0-6         evaluate_0.14        lattice_0.20-41     
 [76] ROCR_1.0-7           labeling_0.3         patchwork_1.0.0.9000
 [79] htmlwidgets_1.5.1    tidyselect_1.0.0     RcppAnnoy_0.0.16    
 [82] plyr_1.8.6           magrittr_1.5         R6_2.4.1            
 [85] gplots_3.0.3         generics_0.0.2       pbdZMQ_0.3-3        
 [88] DBI_1.1.0            pillar_1.4.3         haven_2.2.0         
 [91] withr_2.2.0          fitdistrplus_1.0-14  survival_3.1-11     
 [94] tsne_0.1-3           future.apply_1.4.0   modelr_0.1.6        
 [97] crayon_1.3.4         uuid_0.1-4           KernSmooth_2.23-16  
[100] plotly_4.9.2.1       GetoptLong_0.1.8     readxl_1.3.1        
[103] data.table_1.12.8    reprex_0.3.0         digest_0.6.25       
[106] munsell_0.5.0        viridisLite_0.3.0   
In [ ]: